Computed Tomography Body Composition Research Articles

NCOG-48. CT SCAN COMPOSITE SCORE CAN IDENTIFY GLIOBLASTOMA PATIENTS WITH WORSE OUTCOMES

Abstract OBJECTIVE Obesity is a risk factor for many cancer types including glioblastoma multiforme (GBM). We used CT body composition analyses to construct a sex-specific composite score that would optimally predict overall survival in GBM. METHODS Using a retrospective institutional cohort, we performed single slice (L3) and volumetric (L1-L5) automated body segmentation analyses on abdominal and pelvic computed tomography (CT) scans performed within 1 month of diagnosis of GBM. Multivariable Cox proportional hazards models were then used to identify variables that were independently associated with GBM overall survival. Lastly, a learning-tree algorithm was implemented to identify the relative importance of body composition metrics using a XGBoost-powered learning model. These weights were used to create sex-specific composite CT scan scores of protective and predisposing factor with relation to GBM survival. A log-rank pairwise t-test was used to compared GBM survival across patient groups stratified based on these CT composite scores. RESULTS Higher relative inter/intramuscular adipose tissue volume (HR(95%CI):1.9(1.01-3.06), p=0.02), visceral adipose tissue volume (HR(95%CI):2.1(1.04-4.1), p=0.04) and aortic calcification volume (HR(95%CI):2.6(1.2-5.5), p<0.001) were associated with lower GBM overall survival, while higher subcutaneous adipose tissue volume (HR(95%CI):0.4(0.2-0.8), p=0.02), iliopsoas muscle volume (HR(95%CI):0.4(0.2-0.9), p<0.001), and trabecular bone density (HR(95%CI):0.4(0.1-0.8), p=0.003) were predictive of higher GBM OS (p-value of all tests <0.05). Using quartiles of the CT scan composite predisposing and protective scores, we were able to stratify male and female participants with significantly different outcomes with regards to GBM OS (Figure). Male and female participants with higher burden of predisposing factors had significantly lower chances of survival compared to those with lower cumulative burden of these factors (Figure, p-value Q4 vs. Q1 males< 0.01 and 0.001 in females) CONCLUSION Using automated body segmentation analysis we optimized a CT scan composite score that was able to stratify GBM patients with worse overall survival.

522. MULTI-SLICE, VOLUMETRIC COMPUTED TOMOGRAPHY BODY COMPOSITION ANALYSIS IN PATIENTS WITH OESOPHAGOGASTRIC CANCER

Abstract Background Single-slice computed tomography (CT) body composition has been studied extensively for prognostication in patients with cancer. New software packages can also provide multi-slice volumetric measurements, but the clinical utility of these remains under explored. This study aimed to evaluate the concordance between single- and multi-slice body composition analyses in patients with oesophagogastric cancer, and to explore the association between these measures and overall survival. Methods Consecutive patients with newly diagnosed oesophagogastric cancer were identified through the prospectively maintained regional database of the South-East Scotland Cancer Network across a 2-year study period. CT body composition analyses were undertaken using scans collected during routine clinical care. Single-slice (cross-sectional area at mid L3) and multi-slice (volume between T12-L4) measurements were obtained for skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT). Agreement between sex-stratified z-scores was quantified using Pearson correlation coefficients and Bland–Altman analyses. Cox proportional hazard modelling was used to estimate the effect of these measures on overall survival. Results Overall, 504 patients (67.9% male, median 72 years) were newly diagnosed with oesophagogastric cancer during the study period. Treatment was commenced with curative intent for 131 patients (26.0%). Single-slice area and multi-slice measurements were highly correlated for SKM (R: 0.97, p<0.001), SAT (R: 0.98, p<0.001) and VAT (R: 0.97, p<0.001), SKM radiodensity (R: 0.93, p<0.001) and IMAT (R: 0.88, p<0.001). Bias on Bland-Altman analysis was 0.00 for all tissue measurements. Limits of agreement (LoA) were narrowest for SAT (± 0.43), VAT (± 0.46) and SKM (± 0.48), but slightly wider for SKM radiodensity (± 0.73) and IMAT (± 0.96). Adipose tissue ‘outliers’ (those where agreement between single- and multi-slice z-scores was outside the LoA) had a higher median weight and body mass index (BMI), suggestive of poorer agreement in patients with obesity. Sensitivity analysis, excluding those with BMI >30, narrowed the LoA for SKM, VAT, SAT and IMAT. Direction and magnitudes of observed effect sizes for overall survival were all highly comparable, with hazard ratios for each tissue type varying by ≤0.04 between single- and multi-slice adjusted estimates. Conclusions Single-slice and multi-slice assessments of CT body composition provide highly correlated measurements for skeletal muscle, subcutaneous fat, visceral fat, and intra-muscular fat amongst patients with OG cancer. However, agreement between measurements is poorer in patients with obesity. The associations between these tissues measurements and overall survival are comparable across both types of body composition analysis.

Prognostic value of CT body composition analysis for 1-year mortality after transcatheter aortic valve replacement.

To investigate the value of body composition indices derived from pre-procedural computed tomography (CT) in predicting 1-year mortality among patients who underwent transcatheter aortic valve replacement (TAVR). We assessed consecutive patients who underwent TAVR between June 2016 and December 2021 at a single academic medical center. Skeletal muscle and subcutaneous fat area at the T4, T12, and L3 levels on pre-procedural CT were measured. The association between body composition and 1-year mortality was evaluated using Cox proportional hazard regression analysis. Finally, 408 patients were included (185 men and 223 women; mean age, 81.7 ± 5.1 years; range, 62-98 years). Post-procedural death occurred in 13.2% of patients. The muscle-height index and fat-height index at the L3 level were more strongly correlated with those at the T12 level (r = 0.765, p < 0.001 and r = 0.932, p < 0.001, respectively) than with those at the T4 level (r = 0.535, p < 0.001 and r = 0.895, p < 0.001, respectively). The cumulative 1-year mortality rate was highest for patients with both sarcopenia and adipopenia (26%), followed by those with adipopenia only(17%), those with sarcopenia only (12%), and those with neither sarcopenia nor adipopenia (8%, p = 0.002). Multivariable analysis revealed that body composition at the T12 level was an independent risk factor for 1-year mortality (hazard ratio: 4.09, 95% confidence interval: 2.01-8.35) in patients with both sarcopenia and adipopenia (p < 0.001). Sarcopenia or adipopenia assessed with CT at the thoracic level may be valuable for stratifying 1-year all-cause mortality in patients who undergo TAVR. Skeletal muscle and subcutaneous fat mass indices at the level of T12, measured on pre-procedural CT, have value for risk stratification of 1-year all-cause mortality in patients who undergo transcatheter aortic valve replacement. Sarcopenia and adipopenia are associated with the prognosis of patients undergoing transcatheter aortic valve replacement. Body composition at the T12 level was an independent risk factor for 1-year all-cause mortality. Sarcopenia or adipopenia assessed at T12 with pre-procedural CT is valuable for risk stratification.

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality. The derivation set (n=516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n=380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n=10141). The mean ages of the derivation and external sets were 62.8 and 59.7years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P<0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5years, women 79%; median follow-up 4.9years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS+sarcopenia (aHR 1.65), osteoporosis+sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities. A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

Association Between Fat Depletion and Prognosis of Amyotrophic Lateral Sclerosis: CT-Based Body Composition Analysis.

The purpose of this study was to present the results of our investigation of the prognostic value of adipopenia and sarcopenia in patients with amyotrophic lateral sclerosis (ALS). Consecutive patients with ALS with abdominal computed tomography (CT) were retrospectively identified at a single tertiary hospital between January 2010 and July 2021. Deep learning-based volumetric CT body composition analysis software was used to obtain abdominal waist fat volume, fat attenuation, and skeletal muscle area at the L3 level, then normalized to the fat volume index (FVI) and skeletal muscle index (SMI). Adipopenia and sarcopenia were defined as the sex-specific lowest quartile and SMI reference values, respectively. The associations of CT-derived body composition parameters with clinical variables, such as body mass index (BMI) and creatinine, were evaluated by Pearson correlation analyses, and associations with survival were assessed using the multivariable Cox regression analysis. Eighty subjects (40 men, 65.5 ± 9.4 years of age) were investigated (median interval between disease onset and CT examination = 25 months). The mean BMI at the CT examination was 20.3 ± 4.3 kg/m2 . The BMI showed a positive correlation with both FVI (R = 0.70, p < 0.001) and SMI (R = 0.63, p < 0.001), and the serum creatinine level was associated with SMI (R = 0.68, p < 0.001). After adjusting for sex, age, King's stage, BMI, creatinine, progression rate, and sarcopenia, adipopenia was associated with shorter survival (hazard ratio [HR] = 5.94, 95% confidence interval [CI] = 1.01, 35.0, p = 0.049). In a subgroup analysis for subjects with nutritional failure (stage 4a), the HR of adipopenia was 15.1 (95% CI = 2.45, 93.4, p = 0.003). Deep learning-based CT-derived adipopenia in patients with ALS is an independent poor prognostic factor for survival. ANN NEUROL 2023;94:1116-1125.

The Impact of Acute Systemic Inflammation Secondary to Oesophagectomy and Anastomotic Leak on Computed Tomography Body Composition Analyses.

This study aimed to longitudinally assess CT body composition analyses in patients who experienced anastomotic leak post-oesophagectomy. Consecutive patients, between 1 January 2012 and 1 January 2022 were identified from a prospectively maintained database. Changes in computed tomography (CT) body composition at the third lumbar vertebral level (remote from the site of complication) were assessed across four time points where available: staging, pre-operative/post-neoadjuvant treatment, post-leak, and late follow-up. A total of 20 patients (median 65 years, 90% male) were included, with a total of 66 computed tomography (CT) scans analysed. Of these, 16 underwent neoadjuvant chemo(radio)therapy prior to oesophagectomy. Skeletal muscle index (SMI) was significantly reduced following neoadjuvant treatment (p < 0.001). Following the inflammatory response associated with surgery and anastomotic leak, a decrease in SMI (mean difference: -4.23 cm2/m2, p < 0.001) was noted. Estimates of intramuscular and subcutaneous adipose tissue quantity conversely increased (both p < 0.001). Skeletal muscle density fell (mean difference: -5.42 HU, p = 0.049) while visceral and subcutaneous fat density were higher following anastomotic leak. Thus, all tissues trended towards the radiodensity of water. Although tissue radiodensity and subcutaneous fat area normalised on late follow-up scans, skeletal muscle index remained below pre-treatment levels.

Computed tomography body composition and clinical outcomes following lung transplantation in cystic fibrosis

BackgroundLow muscle mass is common in patients approaching lung transplantation and may be linked to worse post-transplant outcomes. Existing studies assessing muscle mass and post-transplant outcomes include few patients with cystic fibrosis (CF).MethodsBetween May 1993 and December 2018, 152 adults with CF received lung transplants at our institution. Of these, 83 met inclusion criteria and had usable computed tomography (CT) scans. Using Cox proportional hazards regression, we evaluated the association between pre-transplant thoracic skeletal muscle index (SMI) and our primary outcome of death after lung transplantation. Secondary outcomes, including days to post-transplant extubation and post-transplant hospital and intensive care unit (ICU) length of stay, were assessed using linear regression. We also examined associations between thoracic SMI and pre-transplant pulmonary function and 6-min walk distance.ResultsMedian thoracic SMI was 26.95 cm2/m2 (IQR 23.97, 31.32) for men and 22.83 cm2/m2 (IQR 21.27, 26.92) for women. There was no association between pre-transplant thoracic SMI and death after transplant (HR 1.03; 95% CI 0.95, 1.11), days to post-transplant extubation, or post-transplant hospital or ICU length of stay. There was an association between pre-transplant thoracic SMI and pre-transplant FEV1% predicted (b = 0.39; 95% CI 0.14, 0.63), with higher SMI associated with higher FEV1% predicted.ConclusionsSkeletal muscle index was low for men and women. We did not identify a significant relationship between pre-transplant thoracic SMI and post-transplant outcomes. There was an association between thoracic SMI and pre-transplant pulmonary function, confirming the potential value of sarcopenia as a marker of disease severity.

Artificial intelligence-based body composition predicts postoperative complications and mortality in advanced heart failure patients receiving long-term mechanical circulatory support

Abstract Background Obesity is an independent cardiovascular risk factor and is associated with higher postoperative infection rates in patients after cardiac surgery. However, in heart failure, obesity has been described as beneficial in terms of survival (“obesity paradox”); whereas sarcopenia is associated with poor prognosis. An increasing number of advanced heart failure patients requires surgical treatment in terms of left ventricular assist device (LVAD) implantation. The postoperative in-hospital survival has improved in recent years but is still relatively high (11.1%). The impact of body composition on outcome in this population remains unclear. Purpose The aim of this investigation was to examine the preoperative computed tomography (CT) body composition based on artificial intelligence (AI) analysis as a predictor of the postoperative outcome in advanced heart failure patients, who underwent LVAD implantation. Methods Preoperative CT scans of 137 patients who received LVAD implantations between 2015 and 2020 were retrospectively analyzed using an AI-powered automated software tool based on a convolutional neural network, U-net, developed for image segmentation (Visage version 7.1., Visage Imaging GmbH, Berlin, Germany; Figure 1). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT, SAT), psoas and total abdominal muscle areas (PMA, TAMA) and sarcopenia (defined as lumbal skeletal muscle index ≤38.5 cm2/m2 in women and ≤54.4 cm2/m2 in men). The body composition parameters were correlated with postoperative major complication rates and survival. Results The mean age of patients was 58.21±11.9 years; 122 (89.1%) were male. Most patients had severe heart failure requiring inotropes (INTERMACS profile I-III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). 44 (32.1%) patients were obese (BMI ≥30 kg/m2), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of overall postoperative complications (SAT 158.22 cm2 [63.09, 256.78 cm2] vs. 124.04 cm2 [42.86, 187.68 cm2], p=0.049) and in-hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2] vs. 135.42 cm2 [49.44, 227.91 cm2], p=0.033; SAT 227.28 cm2 [139.38, 304.35 cm2] vs. 173.81 cm2 [97.65, 254.16 cm2], p=0.009). Sarcopenia did not influence the postoperative outcome and survival within 1 year post LVAD implantation. Conclusions Preoperative AI-based CT body composition identifies patients with poor prognosis after LVAD implantation. Adipose tissue areas predict in-hospital mortality, thus, obesity as a perioperative risk factor outweighs the protective effect known in heart failure patients when undergoing LVAD implantation. Funding Acknowledgement Type of funding sources: None.

Development and validation of a nomogram to predict anastomotic leakage in colorectal cancer based on CT body composition.

BackgroundAnastomotic leakage (AL) is one of the most serious postoperative complications. This study aimed to investigate the predictive value of preoperative body composition for AL in patients with colorectal cancer (CRC).MethodsWe first reviewed data from 3,681 patients who underwent radical CRC resection 2013–2021 in our hospital, and 60 patients were diagnosed with AL after surgery. We designed a nested case-control study and two controls were randomly selected for each case according to the time and position of surgery. Body composition was measured at the level of the third lumbar vertebra based on computed tomography (CT) images. The risk factors of AL were analyzed by univariate and multivariate analysis. Nomogram was built using binary regression analysis and assessed for clinical usefulness, calibration, and discrimination.ResultsIn the multivariate analysis, gender, blood glucose, nutrition risk screening (NRS), skeletal muscle area (SMA) and visceral fat area (VFA) were independent risk factors for developing anastomotic leakage after surgery. The prognostic model had an area under the receiver operating characteristic curve of 0.848 (95% CI, 0.781–0.914). The calibration curve showed good consistency between the predicted and observed outcomes. Decision curve analysis indicated that patients with colorectal cancer can benefit from the prediction model.ConclusionsThe nomogram that combined with gender, blood glucose, NRS, SMA, and VFA had good predictive accuracy and reliability to AL. It may be conveniently for clinicians to predict AL preoperatively and be useful for guiding treatment decisions.

Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography

The primary objective of this study was to compare measurements of skeletal muscle index (SMI), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) at the level of L3, on subjects who underwent computed tomography (CT) and magnetic resonance imaging (MRI) examinations within a three-month period. The secondary objective was to compare the automatic and semi-automatic quantifications of the same values for CT images. Among subjects who underwent CT and MRI at our Institution between 2011 and 2020, exclusion criteria were: presence of extensive artifacts; images not including the whole waist circumference; CT acquired with low-dose technique and lack of non-contrast images. A set of three axial images (CT, MRI T1-weighted and T2-weighted) were used to extract the following measurements with semi-automatic segmentations: SMI [calculated normalizing skeletal muscle area (SMA) by the square height], SAT, VAT. For the CT images only, the same values were also calculated by using automatic segmentation. Statistical analysis was performed comparing quantitative MRI and CT measurements by Pearson correlation analysis and by Bland-Altman agreement analysis. A total of 123 patients were included. By performing linear regression analysis, CT and MRI measurements of SMI showed a high correlation (r2=0.81 for T1, r2=0.89 for T2), with a mean logarithmic difference between CT and MRI quantitative values of 0.041 for T1-weighted and 0.072 for T2-weighted images. CT and MRI measurements of SAT showed high correlation (r2=0.81 for T1; r2=0.81 for T2), with a mean logarithmic difference between CT and MRI values of 0.0174 for T1-weighted and 0.201 for T2-weighted images. CT and MRI measurements of VAT showed high correlation (r2=0.94 for T1; r2=0.93 for T2), with a mean logarithmic difference of 0.040 for T1-weighted and -0.084 for T2-weighted images. The comparison of values extracted by semi-automatic and automatic segmentations were highly correlated. Quantification of body composition values at MRI from T1-weighted and T2-weighted images was highly correlated to same values at CT, therefore quantitative values of body composition among patients who underwent either one of the examinations may be compared. CT body composition values extracted by semi-automatic and automatic segmentations showed high correlation.

Computed Tomography-Based Body Composition in Patients With Ovarian Cancer: Association With Chemotoxicity and Prognosis.

PurposeTo assess the association between computed tomography (CT)-derived quantitative measures of body composition profiling and chemotherapy-related complications, in terms of dose reduction, premature discontinuation of chemotherapy, and cycle delays in patients with ovarian cancer. Secondary purposes were to evaluate associations between sarcopenia and survival, and to evaluate differences in body composition profiling at baseline and after neoadjuvant chemotherapy.Materials and MethodsThe study population was retrospectively selected from a database of patients with newly diagnosed ovarian cancer (any stage) referred to our Institution between Feb 2011 and Mar 2020. Clinical data were recorded, and CT images at the level of the 3rd lumbar vertebra were stored. By using specific software, skeletal muscle area (SMA), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle density (SMD) were extracted. Skeletal muscle index (SMI) was then calculated. Statistical analysis was performed by logistic regression models to identify body composition features predictive of dose reduction, premature end of chemotherapy, and cycle delays. Kaplan-Meier analyses were performed to assess overall survival (OS) and progression-free survival (PFS). The log-rank test was used to determine differences in OS and PFS between sarcopenic and non-sarcopenic patients. Wilcoxon test was performed to compare body composition features before and after neoadjuvant chemotherapy (NACT).ResultsSixty-nine patients were included. A significant association was found between VAT and cycle delays (OR = 1.01, z = 2.01, 95% CI: 1.00–1.02, p < 0.05), between SMA and early discontinuation of chemotherapy (OR = 1.03, z = 2.10, 95% CI: 1.00–1.05, p < 0.05), and between mean SMD and cycle delays (OR = 0.92, z = −2.70, 95%CI: 0.87–0.98, p < 0.01). No significant difference emerged for OS in sarcopenic and non-sarcopenic patients, nor in CT body composition features before and after NACT.ConclusionsIn ovarian cancer patients, CT-derived body composition profiling might predict the risk of chemotoxicity. In particular, VAT and SMD are associated with chemotherapy cycle delays, and SMA with early discontinuation of chemotherapy.

First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine

AbstractBackgroundTo externally evaluate the first picture archiving communications system (PACS)‐integrated artificial intelligence (AI)‐based workflow, trained to automatically detect a predefined computed tomography (CT) slice at the third lumbar vertebra (L3) and automatically perform complete image segmentation for analysis of CT body composition and to compare its performance with that of an established semi‐automatic segmentation tool regarding speed and accuracy of tissue area calculation.MethodsFor fully automatic analysis of body composition with L3 recognition, U‐Nets were trained (Visage) and compared with a conventional image segmentation software (TomoVision). Tissue was differentiated into psoas muscle, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mid‐L3 level images from randomly selected DICOM slice files of 20 CT scans acquired with various imaging protocols were segmented with both methods.ResultsSuccess rate of AI‐based L3 recognition was 100%. Compared with semi‐automatic, fully automatic AI‐based image segmentation yielded relative differences of 0.22% and 0.16% for skeletal muscle, 0.47% and 0.49% for psoas muscle, 0.42% and 0.42% for VAT and 0.18% and 0.18% for SAT. AI‐based fully automatic segmentation was significantly faster than semi‐automatic segmentation (3 ± 0 s vs. 170 ± 40 s, P &lt; 0.001, for User 1 and 152 ± 40 s, P &lt; 0.001, for User 2).ConclusionRapid fully automatic AI‐based, PACS‐integrated assessment of body composition yields identical results without transfer of critical patient data. Additional metabolic information can be inserted into the patient's image report and offered to the referring clinicians.

CT Body Composition of Sarcopenia and Sarcopenic Obesity: Predictors of Postoperative Complications and Survival in Patients with Locally Advanced Esophageal Adenocarcinoma.

Simple SummaryThe incidence of esophageal cancer is constantly rising and patients are often diagnosed at an advanced stage. Surgical resection, if possible, is the curative treatment of choice. However, esophagectomy for cancer is a major surgical procedure and is associated with perioperative morbidity. Preoperative staging examinations are carried out on every patient, and imaging datasets contain valuable information about the patient’s physical condition beyond the routinely assessed tumor extent. In this study, the abdominal muscle and fat mass were quantified during the preoperative staging and postoperative follow-up of 85 patients with locally advanced esophageal adenocarcinoma, and these imaging biomarkers were correlated with surgical complications and patient outcomes. Our analysis showed that sarcopenic patients with low muscle mass were more likely to have major complications and that hospitalization was prolonged, especially in patients with sarcopenic obesity. Low preoperative muscle mass and its decrease during the follow-up also predicted poorer overall survival.Background: To assess the impact of body composition imaging biomarkers in computed tomography (CT) on the perioperative morbidity and survival after surgery of patients with esophageal cancer (EC). Methods: Eighty-five patients who underwent esophagectomy for locally advanced EC after neoadjuvant therapy between 2014 and 2019 were retrospectively enrolled. Pre- and postoperative CT scans were used to assess the body composition imaging biomarkers (visceral (VAT) and subcutaneous adipose tissue (SAT) areas, psoas muscle area (PMA) and volume (PMV), total abdominal muscle area (TAMA)). Sarcopenia was defined as lumbar skeletal muscle index (LSMI) ≤38.5 cm2/m2 in women and ≤52.4 cm2/m2 in men. Patients with a body mass index (BMI) of ≥30 were considered obese. These imaging biomarkers were correlated with major complications, anastomotic leakage, postoperative pneumonia, duration of postoperative hospitalization, disease-free survival (DFS), and overall survival (OS). Results: Preoperatively, sarcopenia was identified in 58 patients (68.2%), and sarcopenic obesity was present in 7 patients (8.2%). Sarcopenic patients were found to have an elevated risk for the occurrence of major complications (OR: 2.587, p = 0.048) and prolonged hospitalization (32 d vs. 19 d, p = 0.040). Patients with sarcopenic obesity had a significantly higher risk for postoperative pneumonia (OR: 6.364 p = 0.018) and a longer postoperative hospital stay (71 d vs. 24 d, p = 0.021). Neither sarcopenia nor sarcopenic obesity was an independent risk factor for the occurrence of anastomotic leakage (p > 0.05). Low preoperative muscle biomarkers (PMA and PMV) and their decrease (ΔPMV and ΔTAMA) during the follow-up period significantly correlated with shorter DFS and OS (p = 0.005 to 0.048). Conclusion: CT body composition imaging biomarkers can identify high-risk patients with locally advanced esophageal cancer undergoing surgery. Sarcopenic patients have a higher risk of major complications, and patients with sarcopenic obesity are more prone to postoperative pneumonia. Sarcopenia and sarcopenic obesity are both subsequently associated with a prolonged hospitalization. Low preoperative muscle mass and its decrease during the postoperative follow-up are associated with lower DFS and OS.

Relation Between Body Composition, Systemic Inflammatory Response, and Clinical Outcomes in Patients Admitted to an Urban Teaching Hospital with COVID-19.

ABSTRACTBackgroundCOVID-19 has been associated with cases of severe respiratory illness, admissions to intensive therapy units (ITUs), and high mortality rates.ObjectivesThe aim of the present study was to examine the relation between computed tomography- body composition (CT-BC) measurements, systemic inflammation, and clinical outcomes in those with COVID-19.MethodsPatients who presented to our institution between March 17 and May 1, 2020, with a positive PCR test for COVID-19 or characteristic radiological changes, were assessed for inclusion. Data collected included general demographic details, clinicopathological variables, poGPS, NLR , CT-BC measurements, and clinical outcomes including ITU admission and 30-d mortality, of those admitted.ResultsSixty-three patients met the study inclusion criteria. Forty-two patients (67%) were aged ≥70 y, 30 (47.6%) were male and 34.9% ( n = 22) had a poGPS ≥1. ITU admission was significantly associated with a high VFA ( P < 0.05). Thirty-day mortality was associated with high VFA (P < 0.05) and low SMI (P < 0.05).ConclusionsSarcopenia in the presence of obesity was associated with clinical outcomes including greater 30-d mortality.

Visceral-to-subcutaneous fat ratio independently predicts the prognosis of locally advanced gastric cancer----- highlighting the role of adiponectin receptors and PPARα, β/ δ, ɤ

BackgroundResults of computed tomography body composition (CTBC) predicting long-term outcomes of gastric cancer have been mixed and the plausible mechanism remains elusive. MethodsWe retrospectively enrolled a cohort of stage III gastric cancer who had undergone curative-intent gastrectomy. Clinicopathological variables, preoperative CTBC including abdominal muscle, subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), and nutritional and inflammatory index were taken together to construct prognostic analysis. In vitro tests using co-culture system of gastric cancer cell lines and visceral adipocytes were conducted. ResultsA total of 191 eligible patients were enrolled. By multivariate analysis, SAT and VAT/ SAT ratio were prognostic factors of disease-free survival, while sarcopenia was not. SAT remained as a prognostic factor of overall survival. SAT index was positively correlated with prognostic nutritional index, while VAT HU was positively correlated with platelet-to-lymphocyte ratio. Expression of adiponectin receptor 1 and 2 (AdipoR1, R2), and peroxisome proliferator-activated receptor (PPAR) α, β/δ, ɤ of patients with higher VAT/SAT ratio were decreased as compared to those with lower VAT/SAT ratio. Proliferation of gastric cancer cells co-cultured with adipocytes was increased by 50–100% and accompanied by down-regulation of mRNAs of AdipoR1, 2, PPARα, β/δ, ɤ, and pro-apoptotic genes, as compared to their controls. ConclusionSAT and VAT played exactly opposite prognostic roles of locally advanced gastric cancers, which might work through modulation of AdipoR1, 2 and PPARα, β/δ, ɤ. Preoperative CTBC, supplementary to classic TNM system, helps clinicians tailor individualized adjuvant therapy and/or nutritional support.

Clinical outcomes associated with computed tomography-based body composition measures in lung transplantation: a systematic review.

Computed tomography (CT) is gaining increased recognition in the assessment of body composition in lung transplant (LTx) candidates as a prognostic marker of post-transplant outcomes. This systematic review was conducted to describe the methodology of CT measures of body composition used in LTx patients and its association with post-transplant outcomes. Six databases were searched (inception-April 2020) for studies of adult LTx patients with thoracic or abdominal CT measures [muscle cross-sectional area (CSA) and/or adiposity]. Thirteen articles were included with 1911 LTx candidates, 58% males, mean age range (48-61years) and body mass index of 21.0-26.1kg/m2 . Several methods were utilized using thoracic or abdominal CT scans to assess skeletal muscle (n=11) and adiposity (n=4) at various anatomic locations (carina, thoracic, and lumbar vertebrae), differing muscle groups, and adipose tissue compartments. Low muscle mass was associated with adverse outcomes in 6/11 studies, including longer mechanical ventilation days (n=2), intensive care (n=2) and hospital stay (n=2), and mortality (n=4). Greater subcutaneous and mediastinal fat were associated with increased risk of primary graft dysfunction (n=2), but implications of adiposity on survival were variable across four studies. Further standardization of CT body composition assessments is needed to assess the prognostic utility of these measures on LTx outcomes.

Myosteatosis is associated with poor physical fitness in patients undergoing hepatopancreatobiliary surgery

Background: Body composition assessment, measured using single computed tomography (CT) slide at L3 level, and aerobic physical fitness, objectively measured using cardiopulmonary exercise testing (CPET), are each independently used for perioperative risk assessment. Both sarcopenia (i.e. low skeletal muscle mass), myosteatosis (i.e. low skeletal muscle radiation attenuation) and impaired objectively measured aerobic fitness (reduced oxygen uptake) have been associated with poor post-operative outcomes and survival in various cancer types. However, the association between CT body composition and physical fitness has not been explored. In this study, we assessed the association of CT body composition with selected CPET variables in patients undergoing hepatobiliary and pancreas surgery. Methods: A pragmatic prospective cohort of 123 patients undergoing hepatobiliary and pancreas surgery were recruited. All patients underwent preoperative CPET. Preoperative CT-scans were analysed using a single CT-slice at L3 level. Multivariate linear regression was used to test the association between CPET variables and body composition. Main outcomes were oxygen uptake at anaerobic threshold (VO2 at AT), oxygen uptake at peak exercise (VO2 peak), skeletal muscle mass and skeletal muscle radiation attenuation (SM-RA). Results: One-hundred-and-thirteen patients were included. Of the CT-body composition variables, SM-RA had the strongest correlation with VO2 peak (r=0.57, p<0.001) and VO2 at AT (r=0.45, p<0.001) while skeletal muscle mass was only weakly associated with VO2 peak (r=0.24, p<0.010). In multivariate analysis, only SM-RA was associated with VO2 Peak (B=0.25, 95%-CI 0.15-0.34, p<0.001, R2=0.42) and VO2 at AT (B=0.13, 95%-CI 0.06-0.18, p<0.001, R2=0.26). Conclusion: There is a positive association between preoperative CT SM-RA and preoperative physical fitness (VO2 at AT and at Peak). This study demonstrates that myosteatosis, and not sarcopenia, is associated with reduced aerobic physical fitness. Combining both myosteatosis and physical fitness variables may provide additive risk stratification accuracy and guide interventions during the perioperative period.

Myosteatosis is associated with poor physical fitness in patients undergoing hepatopancreatobiliary surgery.

BackgroundBody composition assessment, measured using single‐slice computed tomography (CT) image at L3 level, and aerobic physical fitness, objectively measured using cardiopulmonary exercise testing (CPET), are each independently used for perioperative risk assessment. Sarcopenia (i.e. low skeletal muscle mass), myosteatosis [i.e. low skeletal muscle radiation attenuation (SM‐RA)], and impaired objectively measured aerobic fitness (reduced oxygen uptake) have been associated with poor post‐operative outcomes and survival in various cancer types. However, the association between CT body composition and physical fitness has not been explored. In this study, we assessed the association of CT body composition with selected CPET variables in patients undergoing hepatobiliary and pancreatic surgery.MethodsA pragmatic prospective cohort of 123 patients undergoing hepatobiliary and pancreatic surgery were recruited. All patients underwent preoperative CPET. Preoperative CT scans were analysed using a single‐slice CT image at L3 level to assess skeletal muscle mass, adipose tissue mass, and muscle radiation attenuation. Multivariate linear regression was used to test the association between CPET variables and body composition. Main outcomes were oxygen uptake at anaerobic threshold ( V˙O2 at AT), oxygen uptake at peak exercise ( V˙O2 peak), skeletal muscle mass, and SM‐RA.ResultsOf 123 patients recruited [77 men (63%), median age 66.9 ± 11.7, median body mass index 27.3 ± 5.2], 113 patients had good‐quality abdominal CT scans available and were included. Of the CT body composition variables, SM‐RA had the strongest correlation with V˙O2 peak (r = 0.57, P < 0.001) and V˙O2 at AT (r = 0.45, P < 0.001) while skeletal muscle mass was only weakly associated with V˙O2 peak (r = 0.24, P < 0.010). In the multivariate analysis, only SM‐RA was associated with V˙O2 peak (B = 0.25, 95% CI 0.15–0.34, P < 0.001, R 2 = 0.42) and V˙O2 at AT (B = 0.13, 95% CI 0.06–0.18, P < 0.001, R 2 = 0.26).ConclusionsThere is a positive association between preoperative CT SM‐RA and preoperative physical fitness ( V˙O2 at AT and at peak). This study demonstrates that myosteatosis, and not sarcopenia, is associated with reduced aerobic physical fitness. Combining both myosteatosis and physical fitness variables may provide additive risk stratification accuracy and guide interventions during the perioperative period.

Validation study of a new semi-automated software program for CT body composition analysis.

Computed tomography (CT) has been increasingly used to quantify abdominal muscle and fat in clinical research studies, and multiple studies have shown importance of body composition in predicting clinical outcome. The purpose of study is to compare newly developed semi-automated software (BodyCompSlicer) to commercially available validated software (Slice-O-Matic) for CT body composition analysis. CT scans of abdomen at L3 level in 30 patients were analyzed by two reviewers and using two softwares (BodyCompSlicer and Slice-O-Matic). Body composition analysis using BodyCompSlicer was semi-automated. The program automatically segmented subcutaneous fat (SF), skeletal muscle (SM), and visceral fat (VF) areas. Reviewers manually corrected the segmentation using computer-mouse interface as necessary. Body composition analysis using Slice-O-Matic was performed by manually segmenting each area using computer-mouse interface (brush tool). After segmentation, SM, SF, and VF areas were calculated using CT attenuation thresholds. Inter-observer and inter-software variability of measurements were analyzed using intraclass correlation coefficients (ICC) and coefficient of variation (COV). Inter-observer ICC and COV using BodyCompSlicer were 0.997 and 1.5% for SM, 1.000 and 0.8% for SF, and 1.000 and 1.0% for VF, whereas those using Slice-O-Matic were 0.993 and 2.5% for SM, 0.995 and 3.1% for SF, and 0.999 and 2.3% for VF. Inter-software ICCs and COV were 0.995-0.995 and 2.0-2.1% for SM, 0.991-0.994 and 3.4-3.9% for SF, and 0.998-0.998 and 2.8-3.3% for VF. Time to analyze 30 cases was 70-100min and 150-180min using BodyCompSlicer and Slice-O-Matic, respectively. BodyCompSlicer is comparable to Slice-O-Matic for CT body composition analysis.

Legacy effects of short-term intentional weight loss on total body and thigh composition in overweight and obese older adults.

Objective:Weight regain following intentional weight loss may negatively impact body composition, accelerating fat regain and increasing risk of physical disability. The purpose of this study was to compare long-term changes in whole body and thigh composition in obese older adults who intentionally lost and then partially regained weight to obese older adults who remained weight stable.Subjects/Methods:This pilot study analyzed total body (dual-energy X-ray absorptiometry (DXA)) and thigh (computed tomography (CT)) composition data collected from 24 older (65–79 years) adults 18 months after completion of a 5-month randomized trial that compared resistance training alone (RT) with RT plus caloric restriction (RT+CR).Results:Mean loss of body mass in the RT+CR group (n=13) was 7.1±2.4 kg during the 5-month intervention (74% fat mass; 26% lean mass; all P<0.01), whereas RT (n=11) remained weight stable (+0.3±1.8 kg; P=0.64). Differential group effects were observed for all DXA and CT body composition measures at 5 months (all P⩽0.01); however, by 23 months, group differences persisted only for total body (RT+CR: 81.6±10.0 kg vs RT: 88.5±14.9 kg; P=0.03) and lean (RT+CR: 50.8±9.3 kg vs RT: 54.4±12.0 kg; P<0.01) mass. All RT+CR participants regained weight from 5 to 23 months (mean gain=+4.8±2.6 kg; P<0.01). Total fat mass and all thigh fat volumes increased, whereas thigh muscle volume decreased, during the postintervention follow-up in RT+CR (all P⩽0.01). In the RT group, body mass did not change from 5 to 23 months (−0.2±0.9 kg; P=0.87). Decreased total thigh volume, driven by the loss of thigh muscle volume, were the only postintervention body composition changes observed in the RT group (both P<0.04).Conclusions:Short-term body composition benefits of an RT+CR intervention may be lost within 18 months after completion of the intervention.